This disclosure relates generally to the field of image processing, and more particularly to an approach for processing hyperspectral imaging data to detect targets while the data is in a highly compressed form.
In many conventional image processing scenarios comprising hyperspectral imaging (HSI) systems, hyperspectral sensors collect data of an image from one spatial line and disperse the spectrum across a perpendicular direction of the focal plane of the optics receiving the image. Thus, a focal plane pixel measures the intensity of a given spot on the ground in a specific waveband. A complete HSI cube scene is formed by scanning this spatial line across the scene that is imaged. The complete HSI cube may be analyzed as a measurement of the spectrum, the intensity in many wavebands, for a spatial pixel. This spatial pixel or “scene pixel” represents a given spot on the ground in a cross-scan direction for one of the lines at a given time in the scan direction.
Hyperspectral imaging data is large in size making it challenging to store and/or send. Typical data size is on the order of 0.1-1 GB per frame, and a frame can be collected in 1-4 seconds; so in a mission lasting many hours 1-10 TB of data can be collected on a mission. There are ways to compress the data into a smaller form making it easier to store and/or send. These data compression solutions, however, do not address the problem of detecting material of interests or targets in compressed hyperspectral imaging data.
Prior detection approaches include first decompressing a compressed scene pixel into an approximation of the original, full-dimensional scene pixel. And then comparing the scene pixel against spectral references (each representing a material of interest) using conventional spectral detection algorithms, such as Matched Filter (MF) Adaptive Cosine/Coherence Estimator (ACE), and to some extent, Reed-Xiaoli (RX) anomaly detection. Based on the comparison, a determination of whether the scene pixel contains a particular material of interest (or combination material of interests) is made (i.e., a detection).
Expanding a compressed scene pixel back into the original scene pixel adds steps to the detection process. These steps require additional computing resources and burden an already computationally intensive detection process. The non-literal nature of HSI requires many processing steps for every measured scene pixel in which the processing steps are often vector or matrix operations. Therefore, subsequent full-dimensional processing also requires more computations than reduced-dimension processing. Accordingly, there is a need to reduce the amount of processing needed to detect material of interests in hyperspectral imaging data that is in a highly compressed form.
In accordance with an example, a method for detecting materials in a hyperspectral scene containing a plurality of pixels is provided. The method includes, in a detecting engine provided with a spectral reference (S) and, for a given compressed scene pixel, a set of M basis vector coefficients, set of N basis vectors with M less than or equal to N, and code linking the M basis vector coefficients to the N basis vectors. The spectral reference (S) is representative of a material of interest. The method further includes reducing the spectral reference (S) to an N-dimensional spectral reference (SN) based on the set of N basis vectors. The method further includes computing an N-dimensional spectral reference detection filter (SN*) from the N-dimensional spectral reference (SN) and the inverse of an N-dimensional scene covariance (CN) of a hyperspectral scene in which the given compressed scene pixel is seen. The method further includes for each pixel, forming an M-dimensional spectral reference detection filter (SM*) by selecting M elements of the N-dimensional spectral reference detection filter (SN*) based on the code of the given compressed scene pixel linking the M basis vector coefficients to the N basis vectors. The method further includes computing a detection filter score for the given compressed scene pixel based on the M-dimensional spectral reference detection filter (SM*). The detection filter score being indicative of a likelihood the spectrum of the given compressed scene pixel matches the spectrum of the spectral reference (S). The method further includes comparing the detection filter score to a threshold and determining, based on the comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.
In accordance with another example, a system for detecting materials in a hyperspectral scene containing a plurality of pixels is provided. The system includes memory having computer executable instructions thereupon and at least one interface receiving a spectral reference (S) representative of a materials of interest and, for a given compressed scene pixel, a set of M basis vector coefficients, a set of N basis vectors, and code linking the M basis vector coefficients to the N basis vectors. The system further includes a detecting engine coupled to the memory and the at least one interface. The computer executable instructions when executed by the detecting engine cause the detecting engine to reduce the spectral reference (S) to an N-dimensional spectral reference (SN) based on the set of N basis vectors. The detecting engine further caused to compute an N-dimensional spectral reference detection filter (SN*) from the N-dimensional spectral reference (SN) and the inverse of an N-dimensional scene covariance (CN) of a hyperspectral scene in which the given compressed scene pixel is seen. The detecting engine further caused to form an M-dimensional spectral reference detection filter (SM*) by selecting M elements of the N-dimensional spectral reference detection filter (SN*) based on the code of the given compressed scene pixel linking the M basis vector coefficients to the N basis vectors. The detecting engine further caused to compute a detection filter score for the given compressed scene pixel based on the M-dimensional spectral reference detection filter (SM*). The detection filter score being indicative of a likelihood the spectrum of the given compressed scene pixel matches the spectrum of the spectral reference (S). The detecting engine further caused to compare the detection filter score to a threshold and determine, based on the comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.
In accordance with yet another example, a tangible computer-readable storage medium having computer readable instructions stored therein for detecting materials in a hyperspectral scene containing a plurality of pixels is provided. The computer readable instructions when executed by one or more processors cause the one or more processors, for a given compressed scene pixel having a set of M basis vector coefficients, a set of N basis vectors, and code linking the M basis vector coefficients to the N basis vectors, in which the value of M is less than or equal to the value of N, to reduce a spectral reference (S) to an N-dimensional spectral reference (SN) based on the set of N basis vectors, the spectral reference (S) representative of a materials of interest and provided to the detecting engine. The one or more processors further caused to compute an N-dimensional spectral reference detection filter (SN*) from the N-dimensional spectral reference (SN) and the inverse of an N-dimensional scene covariance (CN) of a hyperspectral scene in which the given compressed scene pixel is seen. The one or more processors further caused to form an M-dimensional spectral reference detection filter (SM*) from the N-dimensional spectral reference detection filter (SN*) based on the code of the given compressed scene pixel linking the M basis vector coefficients to the N basis vectors. The one or more processors further caused to compute a detection filter score for the given compressed scene pixel based on the M-dimensional spectral reference detection filter (SM*), the detection filter score being indicative of a likelihood the spectrum of the given compressed scene pixel matches the spectrum of the spectral reference (S). The one or more processors further caused to compare the detection filter score to a threshold and determine, based on the comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.
In some examples, any of the aspects above can include one or more of the following features.
In other examples of the method, computing the detection filter score includes computing a Matched Filter (MF) score in M dimensions for the given compressed scene pixel, or an Adaptive Cosine/Coherence Estimator (ACE) or Reed-Xiaoli (RX) filter score in M×N dimensions for the given compressed scene pixel.
In some examples of the method, the detection that is determined based on the comparison is a candidate detection and the method further includes computing a Matched Filter (MF), Adaptive Cosine/Coherence Estimator (ACE), other spectral filters or Reed-Xiaoli (RX) score in full dimensions or N dimensions for the candidate detection. The score being indicative of the likelihood that the spectrum of the candidate detection matches the spectrum of the spectral reference (S). The method further includes comparing the MF, ACE, other spectral filters or RX score in full dimensions or N dimensions to a second threshold and determining, based on the second comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.
In other examples of the method, computing the detection filter score includes computing a Matched Filter score in M dimensions (MFM) for the given compressed scene pixel and the method further includes when the compressed scene pixel is a detection based on the comparison of the MFM to the threshold, computing a Adaptive Cosine/Coherence Estimator (ACE) or other spectral filters score in M or more dimensions, including full dimensions, for the compressed scene pixel. The method further includes comparing the ACE or other spectral filters score in M or more dimensions score to a second threshold, and determining, based on the second comparison, whether the material of interest is present in the given compressed scene pixel and is a detection.
In some examples of the method, computing the N-dimensional spectral reference detection filter (SN*) includes multiplying the N-dimensional spectral reference (SN) with the inverse of the N-dimensional scene covariance (CN).
In other examples of the method, forming the M-dimensional spectral reference detection filter (SM*) includes selecting M components from the N-dimensional spectral reference detection filter (SN*) based on the code linking M basis vector coefficients for the pixel to the N basis vectors.
The method further includes computing the N-dimensional scene covariance (CN) based on a set of N basis vector coefficients provided to the detecting engine and associated with the set of N basis vectors of the given compressed scene pixel
These and other features and characteristics, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various Figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of claims. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.